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2 Reliable Sensor Location Sensor self-localization or localization of sources may be attempted using a wide variety of algorithms Most simply, sensor nodes can be told their locations, obtained using standard survey techniques, which themselves rely upon knowing some combination of orientation and the positions of landmarks or beacons such as in GPS [157] With dense deployments of nodes, physical phenomena may be located by the expedient of identifying which set of nodes has signal strength above some threshold and then selecting either (a) the node with the strongest signal as the location or (b) some weighted combination of the nodes above the threshold These techniques have low spatial delity, being on the order of the node separation.



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While it may be argued that if the propagation environment is known, then a weighted combination of signal strengths should be highly accurate, in practice most environments in which we would employ sensor networks do not have such simple propagation laws, or else we would simply have deployed a very small number of trusted long-range sensors A better estimate of source position may be obtained by coherent beamforming [100, 101], in which either (a) several clusters of nodes form bearing angles toward the source and then a least squares estimate is made of the intersection point or (b) a single cluster directly estimates position, also usually with some type of least squares estimate The latter technique is accurate only when the source lies within the convex hull of the nodes performing the sensing, while the former can be successful at considerable range.





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Since a wavefront will typically only be coherent within some local region, this implies that multiple clusters are usually needed for distant sources Note that the price paid for the superior capabilities of coherent beamforming is that the nodes must be tightly synchronized, with absolute error among the clocks in the cluster being a small fraction of the period of oscillation of the waves in question Large collections of nodes can locate themselves with respect to a relative coordinate system without outside assistance, but require some landmarks or known positions to establish absolute location with respect to absolute coordinates A noncoherent approach to (approximate) localization of nodes begins with certain nodes with known positions recording the number of communication hops to other nodes in the network.

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If these are at the periphery of the network, then nodes can estimate position with respect to these xed nodes, assuming that the line of propagation to.

Removing the noise segment L involves the following steps: (a) Delete all branch nodes and branch tips in L . (b) Decrease f t p by one for any junction p in L . Based on the new value of f t p , the junction p is either deleted (if f t p = 0 or transformed into a tip or node (if f t p = 1 . A change in the status of a junction p may affect the status of a branch containing p. Thus, a branch segmentation must be performed again after branch filtering is completed to properly register all skeleton voxels and branches. Figure 20.7 shows the effect of image noise on the thinning procedure. The resulting raw skeleton exhibits numerous type-1 and type-2 noise branches. As shown in Figure 20.8(a), the above branch filtering procedure effectively filters out both types of noise branch. However, local geometrical

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each is straight and that the propagation losses are constant A least squares estimate can be used to deal with inconsistencies of the estimates, and re nements can be made based upon actual propagation losses between links [102, 103] However, in reality a relatively high density of landmarks will typically be required due to the typically large variations in near-ground propagation If nodes are synchronized, then time distance of arrival techniques can be used to systematically determine the actual distances of nodes from each other, and then geometric relations can be used to snap together the links in a consistent fashion, again using least squares techniques to resolve the imperfections of the distance estimates Such multilateration techniques can construct a relative coordinate system or, given some landmarks, an absolute coordinate system [104].

So, considering that the CPU can only run machine code, how are the popular programming languages such as C++ and Java translated into machine code A text file containing instructions that describe the program in a high-level language is fed into a compiler. A compiler is a program that takes a source file

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